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AI approaches for NMR analysis of biomolecular mixtures

This project is no longer listed on and may not be available.

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  • Full or part time
    Dr A Lisitsa
    Dr I Barsukov
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

This position will remain open until a suitable candidate has been found.

This is an interdisciplinary project (Computer Science and Biochemistry) aimed at the development of novel AI methods for the analysis of Nuclear Magnetic Resonance spectra.

Renewable biomolecules are replacing traditional chemicals to reduce pollution, energy waste and carbon footprint. Nuclear Magnetic Resonance (NMR) provides detailed information on these materials, but the data sets are very complex, making automated computer assisted analysis challenging. The project aims to develop novel AI approach to NMR spectra analysis, based on automated reasoning and machine learning. We propose (1) to explore applications of Constraint Satisfaction/SAT solving methods to the reconstruction of molecular structures from a combination of NMR spectra, (2) to explore supervised machine learning (ML_ approaches, including, but not limited to, relational learning and generative adversary networks, for the recognition of molecular signatures in the spectra of complex mixtures.

To apply for this opportunity, please visit:

Funding Notes

Self-funded students only – no funding is available for this project.

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